LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
This 86th episode of Learning Machines 101 discusses the problem of assigning probabilities to a possibly infinite set of outcomes in a spac...
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Learning Machines 101
Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our v...
Episodes
20 of 85 episodes
LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges
This 85th episode of Learning Machines 101 discusses formal convergence guarantees for a broad class of machine learning algorithms designed...
LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
In this episode of Learning Machines 101, we review Chapter 6 of my book "Statistical Machine Learning" which introduces methods for analyzi...
LM101-083: Ch5: How to Use Calculus to Design Learning Machines
This particular podcast covers the material from Chapter 5 of my new book "Statistical Machine Learning: A unified framework" which is now a...
LM101-082: Ch4: How to Analyze and Design Linear Machines
The main focus of this particular episode covers the material in Chapter 4 of my new forthcoming book titled "Statistical Machine Learning:...
LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
This particular podcast covers the material in Chapter 3 of my new book "Statistical Machine Learning: A unified framework" with expected pu...
LM101-080: Ch2: How to Represent Knowledge using Set Theory
This particular podcast covers the material in Chapter 2 of my new book "Statistical Machine Learning: A unified framework" with expected pu...
LM101-079: Ch1: How to View Learning as Risk Minimization
This particular podcast covers the material in Chapter 1 of my new (unpublished) book "Statistical Machine Learning: A unified framework". I...
LM101-078: Ch0: How to Become a Machine Learning Expert
This particular podcast (Episode 78 of Learning Machines 101) is the initial episode in a new special series of episodes designed to provide...
LM101-077: How to Choose the Best Model using BIC
In this 77th episode of www.learningmachines101.com , we explain the proper semantic interpretation of the Bayesian Information Criterion (B...
LM101-076: How to Choose the Best Model using AIC and GAIC
In this episode, we explain the proper semantic interpretation of the Akaike Information Criterion (AIC) and the Generalized Akaike Informat...
LM101-075: Can computers think? A Mathematician's Response (remix)
In this episode, we explore the question of what can computers do as well as what computers can't do using the Turing Machine argument. Spec...
LM101-074: How to Represent Knowledge using Logical Rules (remix)
In this episode we will learn how to use "rules" to represent knowledge. We discuss how this works in practice and we explain how these idea...
LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
This is a remix of the original second episode Learning Machines 101 which describes in a little more detail how the computer program that A...
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
This podcast is basically a remix of the first and second episodes of Learning Machines 101 and is intended to serve as the new introduction...
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
In this podcast, we provide some insights into the complexity of common sense. First, we discuss the importance of building common sense int...
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering tech...
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
This 69 th episode of Learning Machines 101 provides a short overview of the 2017 Neural Information Processing Systems conference with a fo...
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
This 68th episode of Learning Machines 101 discusses a broad class of unsupervised, supervised, and reinforcement machine learning algorithm...
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
In this episode we discuss how to learn to solve constraint satisfaction inference problems. The goal of the inference process is to infer t...